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how_attentive_are_gats
how_attentive_are_gats PublicCode for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)
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bottleneck
bottleneck PublicCode for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
Repositories
- layer_norm_expressivity_role Public
Code for the paper "On the Expressivity Role of LayerNorm in Transformers' Attention" (Findings of ACL'2023)
- RASP Public
An interpreter for RASP as described in the ICML 2021 paper "Thinking Like Transformers"
- code2seq Public
Code for the model presented in the paper: "code2seq: Generating Sequences from Structured Representations of Code"
- code2vec Public
TensorFlow code for the neural network presented in the paper: "code2vec: Learning Distributed Representations of Code"
- slm-code-generation Public
TensorFlow code for the neural network presented in the paper: "Structural Language Models of Code" (ICML'2020)
- bottleneck Public
Code for the paper: "On the Bottleneck of Graph Neural Networks and Its Practical Implications"
- how_attentive_are_gats Public
Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)
- PigeonJS Public Forked from eth-sri/UnuglifyJS
Demonstration of the path-extraction process shown in the paper "A General Path-Based Representation for Predicting Program Properties"
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